I have 2 numpy arrays as following:

```
#[ 3 5 6 8 8 9 9 9 10 10 10 11 11 12 13 14] #rows
#[11 7 11 4 7 2 4 7 2 4 7 4 7 7 11 11] #cols
```

I want to find all sets of matches e.g:

3 6 13 14 from rows match 11 in cols

5 8 9 10 11 12 from rows match 2 4 7 in cols

Is there a direct numpy way to do this? There are no blank values and row and col size will be same.

What I have tried (loops and not most efficient):

```
#first get array of indices, sorted by unique element
idx_sort = np.argsort(cols)
# sorts records array so all unique elements are together
sorted_records_array = cols[idx_sort]
# returns the unique values, the index of the first occurrence of a value, and the count for each element
vals, idx_start, count = np.unique(sorted_records_array, return_counts=True, return_index=True)
# splits the indices into separate arrays
res = np.split(idx_sort, idx_start[1:])
#Using looping I use intersections and concatenate to group sets:
for cntr,itm in enumerate(res):
idx = rows[itm]
for cntr2,itm2 in enumerate(res):
if cntr != cntr2:
intersectItems = np.intersect1d(rows[itm], rows[itm2])
if intersectItems.size > 0:
#print('intersectItems',intersectItems)
res[cntr] = np.unique(np.concatenate((res[cntr], res[cntr2]), axis=0))
```

I will further need to find and remove duplicates as my output here is [ 3 6 13 14],[11 11 11 11] …

Source: Python Questions